[Hiring] Data Analytics Engineer @IXIS

Role Description

IXIS is seeking a senior-level Data Analytics Engineer to join our Data Analytics Engineering (DAE) team. You will play a key role in managing and evolving our data ingestion, sanitation, and transformation pipelines. Our team handles complex client data, joining Adobe and GA4 clickstream data with social, CRM, and other business data to create metrics and segments that power our data visualization products.

This is a hands-on individual contributor (IC) role. You will be expected to lead by example through high-quality design, coding, and problem-solving and contribution to technical direction.

You will be complementing an experienced team lead and will collaborate with other team members while contributing your own perspective and best practices. We are particularly interested in candidates who have seen different ways of doing things and can help us evolve by improving our data quality, scalability, and overall pipeline performance.

This is a high-impact role where you’ll help shape our technical direction, improve existing systems, and introduce new tools and workflows to make our data products even better.

Success in this role looks like:

• Designing performant data pipelines for ingestion and transformation of complex datasets into usable data products.
• Building scalable infrastructure that supports hourly, daily, and weekly update cycles.
• Implementing automated QA checks and monitoring that catch data anomalies before they reach clients.
• Re-architecting parts of our system to improve performance or reduce cost.
• Supporting team members through code reviews and collaboration.

Responsibilities

• Build enterprise-grade batch and real-time data processing pipelines using AWS with a focus on serverless architectures.
• Design and implement automated ELT processes to integrate disparate datasets.
• Work across multiple teams to ingest, extract, and process data via Python, R, zsh, SQL, REST, and GraphQL APIs.
• Join and transform clickstream and CRM data into meaningful metrics and segments for visualization.
• Create automated acceptance, QA, and reliability checks for business logic and data integrity.
• Design appropriately normalized schemas and determine when to use SQL vs NoSQL solutions.
• Optimize infrastructure and schema design for performance, scalability, and cost.
• Help define and maintain CI/CD and deployment pipelines for data infrastructure.
• Containerize and deploy solutions using Docker and AWS ECS.
• Proactively identify and resolve data discrepancies and implement safeguards to prevent recurrence.
• Contribute to documentation, onboarding materials, and cross-team enablement.

Qualifications

• B.A./B.S. in Computer Science, Software Engineering, or a related field; training in statistics/mathematics/machine learning is a plus.
• 3-5 years of experience building scalable, reliable data pipelines and data products in a cloud environment (AWS preferred).
• Deep understanding of ELT processes and data modeling principles.
• Strong programming skills in Python (or similar scripting languages).
• Advanced SQL skills and intermediate to advanced relational database design experience.
• Familiarity with joining large behavioral datasets like Adobe and GA4 clickstream data.
• Excellent problem-solving skills and attention to data detail.
• Experience managing and prioritizing multiple initiatives with minimal supervision.

Additional Desired Skills

• Experience with dbt or other transformation-layer tools.
• Familiarity with Docker containerization and orchestration.
• Experience with statistical programming (R or Python preferred).
• API design or integration experience for data pipelines.
• Experience developing in a Linux or Mac environment.
• Exposure to data QA frameworks or observability tools (e.g. Great Expectations, Monte Carlo, etc.).

Benefits

• If you’re passionate about turning raw data into reliable, actionable insight—and want to help shape the future of data engineering at a growing SaaS company—we’d love to hear from you.